Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing | Paper Citation Record

Observation 080835a8-8996-40b0-aee3-a7491e9ecc2d · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Semi-supervised classification with graph convolutional networks,

Reference 1

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This paper cites Comprehensive study on zeroing neural network with high-order evolutionary formula, nonlinear functions, and variable parameter for time-changing matrix cholesky decomposition,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Comprehensive study on zeroing neural network with high-order evolutionary formula, nonlinear functions, and variable parameter for time-changing matrix cholesky decomposition,

Reference 2

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This paper cites Nie-gcn: Neighbor item embedding-aware graph convolutional network for recommendation,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Nie-gcn: Neighbor item embedding-aware graph convolutional network for recommendation,

Reference 3

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This paper cites Contrastive graph clustering with adaptive filter,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Contrastive graph clustering with adaptive filter,

Reference 4

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This paper cites Generalizing aggregation functions in gnns: building high capacity and robust gnns via nonlinear aggregation,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Generalizing aggregation functions in gnns: building high capacity and robust gnns via nonlinear aggregation,

Reference 5

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Observation 126f13df-ee83-4d9a-8028-ad6d0fbb4afa · outbound

This paper cites Raw-gnn: Random walk aggregation based graph neural network,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Raw-gnn: Random walk aggregation based graph neural network,

Reference 6

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This paper cites What contributes more to the robustness of heterophilic graph neural networks?.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing What contributes more to the robustness of heterophilic graph neural networks?

Reference 7

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Observation c15b547e-df66-4630-bb04-72b4b5cd2760 · outbound

This paper cites Block modeling- guided graph convolutional neural networks,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Block modeling- guided graph convolutional neural networks,

Reference 8

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Observation 9a83b287-df25-40c7-9468-0ff6df93704e · outbound

This paper cites Heterogeneous graph neural network via attribute completion,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Heterogeneous graph neural network via attribute completion,

Reference 9

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Observation a06b4dd4-db84-4946-8f5f-b24d9677cd1d · outbound

This paper cites Finding global homophily in graph neural networks when meeting heterophily,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Finding global homophily in graph neural networks when meeting heterophily,

Reference 10

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Observation 6d0a6241-84d8-4ebf-86bf-c7dcb01b1cd3 · outbound

This paper cites Adaptive universal generalized pagerank graph neural network,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Adaptive universal generalized pagerank graph neural network,

Reference 11

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Observation 3cbe4fac-4746-4a48-9cd1-49c09822f3a4 · outbound

This paper cites Deformable graph convolu- tional networks,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Deformable graph convolu- tional networks,

Reference 12

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This paper cites Graph pointer neural networks,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Graph pointer neural networks,

Reference 13

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This paper cites Identifying and correcting label bias in machine learning,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Identifying and correcting label bias in machine learning,

Reference 14

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Observation fb7b2e96-e832-4ece-9631-22fc1e5862a6 · outbound

This paper cites Fairness in semi-supervised learning: Unlabeled data help to reduce discrimination,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Fairness in semi-supervised learning: Unlabeled data help to reduce discrimination,

Reference 15

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This paper cites Beyond low-frequency information in graph convolutional networks,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Beyond low-frequency information in graph convolutional networks,

Reference 16

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Observation ddb670d1-d76d-4b41-b096-3fc1ac620e5b · outbound

This paper cites Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks,

Reference 17

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This paper cites Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks,

Reference 18

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Observation cea3ffc9-3d4d-4269-87ba-b05d75b06edf · outbound

This paper cites Deepergcn: Training deeper gcns with generalized aggregation functions,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Deepergcn: Training deeper gcns with generalized aggregation functions,

Reference 19

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Observation 9684613a-e9b5-463b-9d6f-bed1a26cd66e · outbound

This paper cites Inductive representation learning on large graphs,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Inductive representation learning on large graphs,

Reference 20

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This paper cites Multi-view contrastive graph clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Multi-view contrastive graph clustering,

Reference 21

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Observation 9226e0b7-0371-4558-aee6-b47562a3d2f4 · outbound

This paper cites Beyond homophily: Reconstructing structure for graph-agnostic clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Beyond homophily: Reconstructing structure for graph-agnostic clustering,

Reference 22

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Observation f59132a0-b646-4739-93c2-266c355f9bca · outbound

This paper cites Graph data condensation via self- expressive graph structure reconstruction,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Graph data condensation via self- expressive graph structure reconstruction,

Reference 23

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Observation 98a42a07-b10d-4d55-9fa1-1d24668f99a5 · outbound

This paper cites Deepgcns: Can gcns go as deep as cnns?.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Deepgcns: Can gcns go as deep as cnns?

Reference 24

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This paper cites CAST: A correlation- based adaptive spectralacm clustering algorithm on multi-scale data,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing CAST: A correlation- based adaptive spectralacm clustering algorithm on multi-scale data,

Reference 25

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This paper cites Cdc: A simple framework for complex data clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Cdc: A simple framework for complex data clustering,

Reference 26

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This paper cites Multi-scale attributed node embedding,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Multi-scale attributed node embedding,

Reference 27

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This paper cites A critical look at the evaluation of gnns under het- erophily: are we really making progress?.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing A critical look at the evaluation of gnns under het- erophily: are we really making progress?

Reference 28

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This paper cites Deep graph clustering via dual correlation reduction,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Deep graph clustering via dual correlation reduction,

Reference 29

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This paper cites Rethinking graph auto-encoder models for attributed graph clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Rethinking graph auto-encoder models for attributed graph clustering,

Reference 30

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This paper cites Open graph benchmark: Datasets for machine learning on graphs,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Open graph benchmark: Datasets for machine learning on graphs,

Reference 31

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This paper cites Geom-gcn: Geometric graph convolutional networks,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Geom-gcn: Geometric graph convolutional networks,

Reference 32

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This paper cites Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning

Reference 33

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This paper cites Attributed graph clustering: A deep attentional embedding approach,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Attributed graph clustering: A deep attentional embedding approach,

Reference 34

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This paper cites Multi-scale graph attention subspace clustering network,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Multi-scale graph attention subspace clustering network,

Reference 35

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This paper cites Simple spectral graph convolution,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Simple spectral graph convolution,

Reference 36

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Observation 0d6df3e8-6d63-4eae-b5ca-eff0e89af48b · outbound

This paper cites Collaborative decision-reinforced self-supervision for attributed graph clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Collaborative decision-reinforced self-supervision for attributed graph clustering,

Reference 37

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Observation a71d655b-dd5c-41af-9844-ff3e8034dd5b · outbound

This paper cites RWR-GAE: Random Walk Regularization for Graph Auto Encoders.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing RWR-GAE: Random Walk Regularization for Graph Auto Encoders

Reference 38

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This paper cites Learning graph embedding with adversarial training methods,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Learning graph embedding with adversarial training methods,

Reference 39

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This paper cites Deep masked graph node clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Deep masked graph node clustering,

Reference 40

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Observation 82a2a685-fe4a-42db-bfd8-9be9f55f3a6f · outbound

This paper cites Every node is different: Dynamically fusing self-supervised tasks for attributed graph clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Every node is different: Dynamically fusing self-supervised tasks for attributed graph clustering,

Reference 41

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Observation 0086afe1-882b-428a-af28-c8a6c3896ee8 · outbound

This paper cites Contrastive multi-view representation learning on graphs,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Contrastive multi-view representation learning on graphs,

Reference 42

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This paper cites Structural deep clustering network,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Structural deep clustering network,

Reference 43

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Observation 6564abcf-d1c1-42d2-9c3c-4f07359763d0 · outbound

This paper cites Deep fusion clustering network,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Deep fusion clustering network,

Reference 44

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This paper cites Simple contrastive graph clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Simple contrastive graph clustering,

Reference 45

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Observation 88f25e83-c65e-4d14-8e37-27ca668f93e6 · outbound

This paper cites Cluster-guided contrastive graph clustering network,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Cluster-guided contrastive graph clustering network,

Reference 46

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Observation a38dadc9-9d88-40ef-830e-890e39c9cedd · outbound

This paper cites Adaptive graph encoder for attributed graph embedding,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Adaptive graph encoder for attributed graph embedding,

Reference 47

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Observation 0e89a1a7-ed74-4b99-8400-22d3bc02edce · outbound

This paper cites Fine-grained attributed graph clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Fine-grained attributed graph clustering,

Reference 48

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This paper cites Robust graph structure learning under heterophily,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Robust graph structure learning under heterophily,

Reference 49

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Observation 027a0c47-f254-45d1-8b2f-b01c9184f018 · outbound

This paper cites Unsupervised network embedding beyond homophily,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Unsupervised network embedding beyond homophily,

Reference 50

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Observation 97a7af5e-d1ea-4630-ac06-ef0949ac84ee · outbound

This paper cites Bootstrapped representation learning on graphs,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Bootstrapped representation learning on graphs,

Reference 51

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Observation 89f4e04a-8ae9-4e62-8afc-e01a7c7ae615 · outbound

This paper cites Progcl: Rethinking hard negative mining in graph contrastive learning,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Progcl: Rethinking hard negative mining in graph contrastive learning,

Reference 52

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Observation dc7b6756-e835-40ec-9ca8-4ef110d5bb51 · outbound

This paper cites S3gc: scalable self- supervised graph clustering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing S3gc: scalable self- supervised graph clustering,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:55:13.633387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9e551b20-97ea-4689-a0ca-b14dfdd79945 · outbound

This paper cites Dink- net: Neural clustering on large graphs,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Dink- net: Neural clustering on large graphs,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:55:13.617726Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cc17a3d8-af54-46ad-9ec8-be5f0fd9e94d · outbound

This paper cites Exploiting neigh- bor effect: Conv-agnostic gnn framework for graphs with heterophily,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Exploiting neigh- bor effect: Conv-agnostic gnn framework for graphs with heterophily,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:55:13.603883Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bc83f6d6-4609-499e-90f3-c696069039ee · outbound

This paper cites Pc-conv: Unifying homophily and het- erophily with two-fold filtering,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Pc-conv: Unifying homophily and het- erophily with two-fold filtering,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:55:13.589996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b77852a5-3f37-412f-b5f2-5656644d3593 · outbound

This paper cites Deep Graph Contrastive Representation Learning,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Deep Graph Contrastive Representation Learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:55:13.576608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8c49e48c-274d-4137-b1a8-dbfaccbee1d4 · outbound

This paper cites Hetergcl: graph contrastive learning framework on heterophilic graph,.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing Hetergcl: graph contrastive learning framework on heterophilic graph,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:55:13.562720Z

Source-reported events for the cited work

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